Harvester Maintenance Prediction Tool: Machine Learning Model Based on Mechanical Features DOI Creative Commons
Rodrigo Oliveira Almeida, Richardson Barbosa Gomes da Silva, Danilo Simões

и другие.

AgriEngineering, Год журнала: 2025, Номер 7(4), С. 97 - 97

Опубликована: Апрель 1, 2025

One important element influencing the efficiency of automated timber harvesting is harvester maintenance. However, understanding this effect limited, which can lead to more frequent harvest interruptions and consequently higher production costs. Data modeling be used evaluate how mechanical aspects affect maintenance in plantation forests, help with forest planning. This study aimed ascertain if characteristics may utilized develop a high-performance model capable properly forecasting using machine learning. A free web application managers implement approach was also developed as part study. For modeling, we considered eight features status target feature. In default mode, ran 25 popular algorithms through database compared them based on accuracy error metrics. Although combination models performed well, Random Forest better mode an 0.933. addition, generated makes it possible create prediction tool that provides quick visualization feature make informed decisions. Along data from experimental research, will available complete file containing predictive model, well software, both Python language.

Язык: Английский

Harvester Maintenance Prediction Tool: Machine Learning Model Based on Mechanical Features DOI Creative Commons
Rodrigo Oliveira Almeida, Richardson Barbosa Gomes da Silva, Danilo Simões

и другие.

AgriEngineering, Год журнала: 2025, Номер 7(4), С. 97 - 97

Опубликована: Апрель 1, 2025

One important element influencing the efficiency of automated timber harvesting is harvester maintenance. However, understanding this effect limited, which can lead to more frequent harvest interruptions and consequently higher production costs. Data modeling be used evaluate how mechanical aspects affect maintenance in plantation forests, help with forest planning. This study aimed ascertain if characteristics may utilized develop a high-performance model capable properly forecasting using machine learning. A free web application managers implement approach was also developed as part study. For modeling, we considered eight features status target feature. In default mode, ran 25 popular algorithms through database compared them based on accuracy error metrics. Although combination models performed well, Random Forest better mode an 0.933. addition, generated makes it possible create prediction tool that provides quick visualization feature make informed decisions. Along data from experimental research, will available complete file containing predictive model, well software, both Python language.

Язык: Английский

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